High-level cognition, e.g., intelligence, draws on multiple processes, following sequential transitions through a series of neural states. The ease of these transitions depends on the connectome – underlying network of white-matter connections. Yet, the link between connectome, brain state transitions, and cognition is unclear, nor how such a relation changes as people age, across their lifespan. Here, I leverage state-of-the-art methodology from network control theory to link network properties, state transitions, and high-level cognition across the human lifespan.
Creativity is a complex, multidimensional, elusive concept, that is vital to personal and societal needs. In this project, we leverage computational network science methods with machine learning, combined with psycholinguistics to develop a computational model to predict ones’ creative ability level. We analyze a simple semantic fluency task (name all the animals you can think of) as a mental navigation process over a multiplex cognitive network. Features of this mental navigation process are then being used to build creativity prediction and classification models.
